Friday, November 20, 2015

Nikkei: Seiichi Gohshi, professor of the Department of Information Design, the Faculty of Informatics, Kogakuin University, and Fujitsu jointly develop a new technology employed for the "Xevic" image processing engine of Fujitsu's Arrows NX F-02H smartphone, to be released in late November 2015. Unlike commonly-used "reconstructed super resolution" and "learning super resolution," the super-resolution technology being researched by Gohshi uses an original method called "nonlinear signal processing method." A nonlinear function is used to supplement high-frequency components and reproduce high-resolution components that surpass the "Nyquist frequency (half of a sampling frequency)," Gohshi said.

4 comments:

That's great, but is it right? I can produce results higher than Nyquist limit, too, simply by injecting in random data above the Nyquist limit. I would probably want to see a ground truth vs reconstructed and corresponding error at the high frequencies.

Their papers talk about creating above-Nyquist frequencies by making the edges steeper, and proceed by calling it "super-resolution", based on a naive idea that additional spatial frequencies equal higher image resolution. Sharpening is something that has been used in all cameras and TV sets for 60+ years. A sharpened "vertical" edge has almost infinite frequency spectrum, for sure, but image resolution is not improved, only acutance. Acutance can be changed post-capture, resolution is a much different story related to sampling in the image sensor. In contrast, there are methods of computational super-resolution that require heavy-duty image processing to extract additional image detail based on a variety of image priors, such as sparsity in certain mathematical domains, non-local self-similarity, simlarity across different image scales, and other image priors. The very fact that their publications never provide any objective measure of their "super-resolution" enhancement, such as PSNR relative to a ground truth high-resolution image, is peculiar. This kind of enhancement counts on subjective perception of increased sharpness while never increasing the actual image resolution. At best this is a misuse of the established terminology, or a plain misrepresentation.